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Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

dc.contributor.authorAkyol, Kemal
dc.contributor.authorŞen, Baha
dc.date.accessioned2026-01-04T15:36:10Z
dc.date.issued2021-07-27
dc.description.abstractThe main aim of this study is to present an effective and distinct deep learning-based model that shows a successful performance in Covid-19 disease which has adversely affects humanity since 2020.Here, the performances of two deep learning architectures on the deep features are compared for Covid-19 disease.<br>In this context, the main contribution of this study is as follows:a) The effectiveness of the Deep Neural Networks (DNN) and Bidirectional Long Short Term Memory (Bi-LSTM) deep learning models were compared comprehensively.b) The performances of the models within the framework of the 5-fold cross-validation technique were verified on the validation datasets.Finally, a highly efficient deep learning model was derived for detecting Covid-19 disease.
dc.description.urihttps://doi.org/10.1007/s12539-021-00463-2
dc.description.urihttps://link.springer.com/content/pdf/10.1007/s12539-021-00463-2.pdf
dc.description.urihttps://dx.doi.org/10.6084/m9.figshare.15162072
dc.description.urihttps://dx.doi.org/10.6084/m9.figshare.15162072.v1
dc.description.urihttps://pubmed.ncbi.nlm.nih.gov/34313974
dc.description.urihttp://dx.doi.org/10.1007/s12539-021-00463-2
dc.description.urihttps://dx.doi.org/10.1007/s12539-021-00463-2
dc.description.urihttps://avesis.aybu.edu.tr/publication/details/5ad3de4d-439f-4d5f-838f-116e44d56401/oai
dc.identifier.doi10.1007/s12539-021-00463-2
dc.identifier.eissn1867-1462
dc.identifier.endpage100
dc.identifier.issn1913-2751
dc.identifier.openairedoi_dedup___::1a423489aee4d782fc7ce526f290489d
dc.identifier.orcid0000-0002-2272-5243
dc.identifier.orcid0000-0003-3577-2548
dc.identifier.pubmed34313974
dc.identifier.scopus2-s2.0-85111390930
dc.identifier.startpage89
dc.identifier.urihttps://hdl.handle.net/20.500.12597/38937
dc.identifier.volume14
dc.identifier.wos000679875500001
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofInterdisciplinary Sciences: Computational Life Sciences
dc.rightsOPEN
dc.subjectDeep Learning
dc.subjectSARS-CoV-2
dc.subjectX-Rays
dc.subjectCOVID-19
dc.subjectHumans
dc.subjectOriginal Research Article
dc.subjectNeural Networks, Computer
dc.subject.sdg3. Good health
dc.titleAutomatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images
dc.typeArticle
dspace.entity.typePublication
local.import.sourceOpenAire
local.indexed.atWOS
local.indexed.atScopus
local.indexed.atPubMed

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